The proposed formulation additionally gives flexible control between appearance and spatial attributes. The appearance term is readily extendable to accept misaligned feature maps to achieve more semantically faithful 3D reconstruction.
Specifically, we apply the Chamfer texture loss between the (foreground) feature maps extracted with a pre-trained VGG-19 network [1]} from the rendered image and the input image. It is worth noting that the feature-level Chamfer texture loss is somewhat related to the contextual loss [2]}, which addresses the misalignment issue for image transfer. The key difference is that the contextual loss only considers the feature distances but ignores their locations. We compare against the contextual loss in the experiment.
